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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 4 hours ago
months of the appointment commencement) and experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess strong
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Feature engineering and feature store development CI/CD for machine learning workflows Monitoring, maintenance, and retraining of production ML models Collaboration with data scientists to operationalize
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Develop machine learning methods and tools with a specific focus on: Data-Centric AI: Including data attribution, data curation, and privacy preservation for large foundation models (e.g., LLMs and VLMs
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notebook Experience with regular expression natural language processing via Python Experience with machine learning models (e.g. regularization regression, random forests, ensemble methods) Experience with
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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of computer vision algorithms, with a focus on assessing performance in edge deployment settings. Their role will involve supporting model development, running experiments, and analyzing results to evaluate
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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Experience with multimodal machine learning, variational autoencoders, and/or similar machine learning or AI models implemented in Python. Responsible for implementing components of research design analysis
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quality Surrogate Modeling: Building, training, and evaluating machine learning surrogate models to emulate complex seismic behaviors and accelerate forecasting Data Engineering: Populating and managing
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing